Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Jennings, N, Parsons, Stuart, & Pocock, MJO (2008) Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks. Canadian Journal of Zoology, 86(5), pp. 371-377.

View at publisher


Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Impact and interest:

18 citations in Scopus
14 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 79762
Item Type: Journal Article
Refereed: Yes
DOI: 10.1139/Z08-009
ISSN: 1480-3283
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2008 N R C Research Press.
Deposited On: 20 Jan 2015 22:53
Last Modified: 22 Jan 2015 23:10

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page